Some pioneer works to study the zero-shot learning for semantic segmentation. ZS3Net [1]} uses generative models to synthesize pixel-level features by word embeddings of unseen classes. CSRL [2]} further incorporating the structural relation in feature synthesize. CaGNet [3]}, [4]} introduce a contextual module for better feature generation.
Different from  [1]}, [2]}, [3]}, SPNet [8]} attempt to mapping vision feature to the semantic space via word embedding. JoEm [9]} a joint embedding strategy between the vision encoder and semantic encoder. In [10]}, variational mapping is used to learn semantic features. In [11]}, the uncertainty-aware losses are proposed to eliminate noisy samples. Other works explored other directions or aspects of zero-shot semantic segmentation. In [12]}, the self-training for zero-shot semantic segmentation are carefully studied. In [13]}, [14]}, the transductive learning setting are explored. In [15]} , they explore the utilization of image caption. However, all those methods have not explored the utilization of the vision-language pre-training model in zero-shot semantic segmentation.
